Model-Driven Lightweight Network for CSI Feedback in Time-Varying Massive MIMO Systems

Channel state information (CSI) is vital for massive multiple-input multiple-output (MIMO) systems to provide high channel capacity and energy efficiency. However, the massive antennas will lead to high feedback overhead in frequency division duplex (FDD) MIMO systems. Recent applications of recurrent neural networks have demonstrated promising results of feedback CSI compression but with tremendous complexity. The markovian-based framework reduces the overall complexity but owns the high complexity of the user equipment (UE), which is not suitable for practical deployment. In this work, we aim to reduce the complexity of UE and improve the reconstruction performance. Leveraging the channel temporal correlation, we propose two differential frameworks called DIFNet1 and DIFNet2 to improve feedback accuracy and efficiency. Moreover, We unfold the Iterative Shrinkage-Thresholding Algorithm (ISTA) to provide excellent reconstruction performance. Simulation results demonstrate that the proposed framework improves the reconstruction performance while greatly reducing the complexity of UEs.